Overview

Dataset statistics

Number of variables27
Number of observations4472276
Missing cells4758344
Missing cells (%)3.9%
Duplicate rows366603
Duplicate rows (%)8.2%
Total size in memory955.4 MiB
Average record size in memory224.0 B

Variable types

Categorical8
DateTime2
Numeric16
Unsupported1

Alerts

Dataset has 366603 (8.2%) duplicate rowsDuplicates
VIN has a high cardinality: 3501376 distinct valuesHigh cardinality
TypMot has a high cardinality: 57584 distinct valuesHigh cardinality
TZn has a high cardinality: 6904 distinct valuesHigh cardinality
ObchOznTyp has a high cardinality: 93656 distinct valuesHigh cardinality
Ct has a high cardinality: 146 distinct valuesHigh cardinality
DrTP is highly imbalanced (65.1%)Imbalance
TZn is highly imbalanced (59.5%)Imbalance
DrVoz is highly imbalanced (72.2%)Imbalance
Ct is highly imbalanced (73.5%)Imbalance
VyslSTK is highly imbalanced (76.8%)Imbalance
TypMot has 232791 (5.2%) missing valuesMissing
VyslEmise has 4472276 (100.0%) missing valuesMissing
ZavC is highly skewed (γ1 = 22.97375934)Skewed
Zav9 is highly skewed (γ1 = 60.04823447)Skewed
VIN is uniformly distributedUniform
VyslEmise is an unsupported type, check if it needs cleaning or further analysisUnsupported
Km has 349677 (7.8%) zerosZeros
ZavA has 2174461 (48.6%) zerosZeros
ZavB has 4198756 (93.9%) zerosZeros
ZavC has 4447578 (99.4%) zerosZeros
Zav0 has 4183776 (93.5%) zerosZeros
Zav1 has 3217168 (71.9%) zerosZeros
Zav2 has 4116379 (92.0%) zerosZeros
Zav3 has 4106450 (91.8%) zerosZeros
Zav4 has 3458680 (77.3%) zerosZeros
Zav5 has 3791040 (84.8%) zerosZeros
Zav6 has 2525840 (56.5%) zerosZeros
Zav7 has 4442373 (99.3%) zerosZeros
Zav8 has 4410567 (98.6%) zerosZeros
Zav9 has 4469856 (99.9%) zerosZeros

Reproduction

Analysis started2023-04-01 15:51:14.463006
Analysis finished2023-04-01 15:54:13.756100
Duration2 minutes and 59.29 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

DrTP
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.2 MiB
pravidelná
3131254 
Evidenční kontrola
896898 
opakovaná
 
209081
Před registrací
 
175058
Technická silniční kontrola
 
22520
Other values (9)
 
37465

Length

Max length46
Median length10
Mean length11.929816
Min length3

Characters and Unicode

Total characters53353428
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvidenční kontrola
2nd rowpravidelná
3rd rowpravidelná
4th rowpravidelná
5th rowpravidelná

Common Values

ValueCountFrequency (%)
pravidelná 3131254
70.0%
Evidenční kontrola 896898
 
20.1%
opakovaná 209081
 
4.7%
Před registrací 175058
 
3.9%
Technická silniční kontrola 22520
 
0.5%
Na žádost zákazníka 19270
 
0.4%
Před schvál. tech. způsob. vozidla 6986
 
0.2%
ADR 4747
 
0.1%
Před registrací - opakovaná 4596
 
0.1%
TSK - Opakovaná 788
 
< 0.1%
Other values (4) 1078
 
< 0.1%

Length

2023-04-01T17:54:13.792821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pravidelná 3131254
55.2%
kontrola 919418
 
16.2%
evidenční 896898
 
15.8%
opakovaná 215447
 
3.8%
před 186852
 
3.3%
registrací 179654
 
3.2%
technická 22616
 
0.4%
silniční 22520
 
0.4%
na 19270
 
0.3%
žádost 19270
 
0.3%
Other values (12) 62021
 
1.1%

Most occurring characters

ValueCountFrequency (%)
n 6146937
11.5%
a 4726420
8.9%
e 4424568
8.3%
r 4410076
8.3%
i 4282660
8.0%
v 4257995
8.0%
d 4241568
7.9%
l 4087684
7.7%
á 3415151
 
6.4%
p 3354543
 
6.3%
Other values (27) 10005826
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50975541
95.5%
Space Separator 1202944
 
2.3%
Uppercase Letter 1146983
 
2.1%
Other Punctuation 21594
 
< 0.1%
Dash Punctuation 6366
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 6146937
12.1%
a 4726420
9.3%
e 4424568
8.7%
r 4410076
8.7%
i 4282660
8.4%
v 4257995
8.4%
d 4241568
8.3%
l 4087684
8.0%
á 3415151
6.7%
p 3354543
6.6%
Other values (14) 7627939
15.0%
Uppercase Letter
ValueCountFrequency (%)
E 896898
78.2%
P 186852
 
16.3%
T 23856
 
2.1%
N 19914
 
1.7%
D 5517
 
0.5%
A 4969
 
0.4%
R 4969
 
0.4%
S 1336
 
0.1%
K 1336
 
0.1%
O 1336
 
0.1%
Space Separator
ValueCountFrequency (%)
1202944
100.0%
Other Punctuation
ValueCountFrequency (%)
. 21594
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6366
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52122524
97.7%
Common 1230904
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 6146937
11.8%
a 4726420
9.1%
e 4424568
8.5%
r 4410076
8.5%
i 4282660
8.2%
v 4257995
8.2%
d 4241568
8.1%
l 4087684
7.8%
á 3415151
 
6.6%
p 3354543
 
6.4%
Other values (24) 8774922
16.8%
Common
ValueCountFrequency (%)
1202944
97.7%
. 21594
 
1.8%
- 6366
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47686909
89.4%
None 5666519
 
10.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 6146937
12.9%
a 4726420
9.9%
e 4424568
9.3%
r 4410076
9.2%
i 4282660
9.0%
v 4257995
8.9%
d 4241568
8.9%
l 4087684
8.6%
p 3354543
7.0%
o 2302704
 
4.8%
Other values (21) 5451754
11.4%
None
ValueCountFrequency (%)
á 3415151
60.3%
í 1118534
 
19.7%
č 919418
 
16.2%
ř 186948
 
3.3%
ž 19270
 
0.3%
ů 7198
 
0.1%

VIN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3501376
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Memory size68.2 MiB
004
 
30
001
 
28
011
 
25
027
 
24
0385
 
23
Other values (3501371)
4472146 

Length

Max length25
Median length17
Mean length16.575769
Min length1

Characters and Unicode

Total characters74131416
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2719606 ?
Unique (%)60.8%

Sample

1st row5164
2nd row17473
3rd row17826
4th row9112533
5th row33344

Common Values

ValueCountFrequency (%)
004 30
 
< 0.1%
001 28
 
< 0.1%
011 25
 
< 0.1%
027 24
 
< 0.1%
0385 23
 
< 0.1%
005 22
 
< 0.1%
TEST0000000000001 22
 
< 0.1%
126 21
 
< 0.1%
014 21
 
< 0.1%
048 21
 
< 0.1%
Other values (3501366) 4472039
> 99.9%

Length

2023-04-01T17:54:13.968385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
004 30
 
< 0.1%
001 28
 
< 0.1%
011 25
 
< 0.1%
027 24
 
< 0.1%
0385 23
 
< 0.1%
005 22
 
< 0.1%
test0000000000001 22
 
< 0.1%
048 21
 
< 0.1%
014 21
 
< 0.1%
126 21
 
< 0.1%
Other values (3501335) 4472039
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 7469461
 
10.1%
1 5842555
 
7.9%
2 4563779
 
6.2%
3 4198779
 
5.7%
5 3870541
 
5.2%
6 3807067
 
5.1%
4 3691081
 
5.0%
7 3480913
 
4.7%
8 3198586
 
4.3%
Z 3188148
 
4.3%
Other values (34) 30820506
41.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43059740
58.1%
Uppercase Letter 31000417
41.8%
Dash Punctuation 39297
 
0.1%
Other Punctuation 31959
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 3188148
 
10.3%
B 2373068
 
7.7%
W 2102260
 
6.8%
M 1984640
 
6.4%
A 1942575
 
6.3%
T 1895672
 
6.1%
F 1859926
 
6.0%
J 1549441
 
5.0%
V 1490268
 
4.8%
C 1204070
 
3.9%
Other values (16) 11410349
36.8%
Decimal Number
ValueCountFrequency (%)
0 7469461
17.3%
1 5842555
13.6%
2 4563779
10.6%
3 4198779
9.8%
5 3870541
9.0%
6 3807067
8.8%
4 3691081
8.6%
7 3480913
8.1%
8 3198586
7.4%
9 2936978
 
6.8%
Other Punctuation
ValueCountFrequency (%)
/ 30835
96.5%
. 836
 
2.6%
% 214
 
0.7%
* 68
 
0.2%
, 5
 
< 0.1%
; 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 39297
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43130999
58.2%
Latin 31000417
41.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 3188148
 
10.3%
B 2373068
 
7.7%
W 2102260
 
6.8%
M 1984640
 
6.4%
A 1942575
 
6.3%
T 1895672
 
6.1%
F 1859926
 
6.0%
J 1549441
 
5.0%
V 1490268
 
4.8%
C 1204070
 
3.9%
Other values (16) 11410349
36.8%
Common
ValueCountFrequency (%)
0 7469461
17.3%
1 5842555
13.5%
2 4563779
10.6%
3 4198779
9.7%
5 3870541
9.0%
6 3807067
8.8%
4 3691081
8.6%
7 3480913
8.1%
8 3198586
7.4%
9 2936978
 
6.8%
Other values (8) 71259
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74131416
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7469461
 
10.1%
1 5842555
 
7.9%
2 4563779
 
6.2%
3 4198779
 
5.7%
5 3870541
 
5.2%
6 3807067
 
5.1%
4 3691081
 
5.0%
7 3480913
 
4.7%
8 3198586
 
4.3%
Z 3188148
 
4.3%
Other values (34) 30820506
41.6%
Distinct334
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.2 MiB
Minimum2022-01-01 00:00:00
Maximum2022-11-30 00:00:00
2023-04-01T17:54:14.060098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:54:14.150979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TypMot
Categorical

HIGH CARDINALITY  MISSING 

Distinct57584
Distinct (%)1.4%
Missing232791
Missing (%)5.2%
Memory size68.2 MiB
-
 
110085
BXE
 
39783
ALH
 
35395
CJZ
 
33807
G4FA
 
30264
Other values (57579)
3990151 

Length

Max length17
Median length16
Mean length4.5108767
Min length1

Characters and Unicode

Total characters19123794
Distinct characters106
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26133 ?
Unique (%)0.6%

Sample

1st rowZ 8001
2nd row7303
3rd rowZ 8001
4th rowZ 6701
5th rowZ 7001

Common Values

ValueCountFrequency (%)
- 110085
 
2.5%
BXE 39783
 
0.9%
ALH 35395
 
0.8%
CJZ 33807
 
0.8%
G4FA 30264
 
0.7%
CBZA 27809
 
0.6%
BLS 25876
 
0.6%
ASV 24924
 
0.6%
CHZ 24835
 
0.6%
AQW 24001
 
0.5%
Other values (57574) 3862706
86.4%
(Missing) 232791
 
5.2%

Length

2023-04-01T17:54:14.245523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
115157
 
2.4%
7 50600
 
1.0%
bxe 39784
 
0.8%
alh 35429
 
0.7%
cjz 33809
 
0.7%
d 31151
 
0.6%
g4fa 30265
 
0.6%
m 29850
 
0.6%
k9k 29341
 
0.6%
cbza 27812
 
0.6%
Other values (42065) 4437724
91.3%

Most occurring characters

ValueCountFrequency (%)
A 1394823
 
7.3%
1 1100133
 
5.8%
B 1015587
 
5.3%
D 976808
 
5.1%
C 961207
 
5.0%
4 959152
 
5.0%
F 955260
 
5.0%
0 868670
 
4.5%
2 656182
 
3.4%
623538
 
3.3%
Other values (96) 9612434
50.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11568986
60.5%
Decimal Number 6400027
33.5%
Space Separator 623538
 
3.3%
Other Punctuation 292429
 
1.5%
Dash Punctuation 229656
 
1.2%
Lowercase Letter 2652
 
< 0.1%
Open Punctuation 2248
 
< 0.1%
Close Punctuation 2208
 
< 0.1%
Math Symbol 2035
 
< 0.1%
Modifier Symbol 10
 
< 0.1%
Other values (2) 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1394823
 
12.1%
B 1015587
 
8.8%
D 976808
 
8.4%
C 961207
 
8.3%
F 955260
 
8.3%
H 585926
 
5.1%
E 532985
 
4.6%
M 511063
 
4.4%
Z 434657
 
3.8%
G 417431
 
3.6%
Other values (30) 3783239
32.7%
Lowercase Letter
ValueCountFrequency (%)
t 334
12.6%
o 303
 
11.4%
p 298
 
11.2%
a 228
 
8.6%
b 151
 
5.7%
c 139
 
5.2%
d 129
 
4.9%
f 127
 
4.8%
s 86
 
3.2%
l 80
 
3.0%
Other values (21) 777
29.3%
Other Punctuation
ValueCountFrequency (%)
. 241102
82.4%
/ 33091
 
11.3%
, 9430
 
3.2%
* 8172
 
2.8%
? 269
 
0.1%
; 182
 
0.1%
: 159
 
0.1%
" 9
 
< 0.1%
@ 5
 
< 0.1%
& 4
 
< 0.1%
Other values (3) 6
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1100133
17.2%
4 959152
15.0%
0 868670
13.6%
2 656182
10.3%
6 588561
9.2%
3 507884
7.9%
7 457591
7.1%
9 433180
 
6.8%
8 419557
 
6.6%
5 409117
 
6.4%
Modifier Symbol
ValueCountFrequency (%)
´ 5
50.0%
¨ 3
30.0%
˙ 2
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 2207
> 99.9%
] 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 2034
> 99.9%
| 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
623538
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 229656
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2248
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11571638
60.5%
Common 7552156
39.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1394823
 
12.1%
B 1015587
 
8.8%
D 976808
 
8.4%
C 961207
 
8.3%
F 955260
 
8.3%
H 585926
 
5.1%
E 532985
 
4.6%
M 511063
 
4.4%
Z 434657
 
3.8%
G 417431
 
3.6%
Other values (61) 3785891
32.7%
Common
ValueCountFrequency (%)
1 1100133
14.6%
4 959152
12.7%
0 868670
11.5%
2 656182
8.7%
623538
8.3%
6 588561
7.8%
3 507884
6.7%
7 457591
6.1%
9 433180
 
5.7%
8 419557
 
5.6%
Other values (25) 937708
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19118036
> 99.9%
None 5756
 
< 0.1%
Modifier Letters 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1394823
 
7.3%
1 1100133
 
5.8%
B 1015587
 
5.3%
D 976808
 
5.1%
C 961207
 
5.0%
4 959152
 
5.0%
F 955260
 
5.0%
0 868670
 
4.5%
2 656182
 
3.4%
623538
 
3.3%
Other values (73) 9606676
50.2%
None
ValueCountFrequency (%)
Š 4069
70.7%
Č 1014
 
17.6%
Á 213
 
3.7%
Ř 127
 
2.2%
Ý 108
 
1.9%
Í 86
 
1.5%
Ž 42
 
0.7%
š 29
 
0.5%
ý 22
 
0.4%
Ě 21
 
0.4%
Other values (12) 25
 
0.4%
Modifier Letters
ValueCountFrequency (%)
˙ 2
100.0%

TZn
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct6904
Distinct (%)0.2%
Missing50
Missing (%)< 0.1%
Memory size68.2 MiB
ŠKODA
1104649 
VOLKSWAGEN
313109 
FORD
284172 
PEUGEOT
 
190113
RENAULT
 
190045
Other values (6899)
2390138 

Length

Max length30
Median length28
Mean length5.9397998
Min length1

Characters and Unicode

Total characters26564127
Distinct characters118
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3016 ?
Unique (%)0.1%

Sample

1st rowBSS
2nd rowSTS HUMENNE
3rd rowSTS HUMENNE
4th rowBSS
5th rowZETOR

Common Values

ValueCountFrequency (%)
ŠKODA 1104649
24.7%
VOLKSWAGEN 313109
 
7.0%
FORD 284172
 
6.4%
PEUGEOT 190113
 
4.3%
RENAULT 190045
 
4.2%
MERCEDES-BENZ 142388
 
3.2%
CITROËN 142197
 
3.2%
VW 135429
 
3.0%
HYUNDAI 133621
 
3.0%
BMW 118897
 
2.7%
Other values (6894) 1717606
38.4%

Length

2023-04-01T17:54:14.341434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
škoda 1104824
24.0%
volkswagen 313111
 
6.8%
ford 284181
 
6.2%
renault 190165
 
4.1%
peugeot 190114
 
4.1%
mercedes-benz 142422
 
3.1%
citroën 142197
 
3.1%
vw 135429
 
2.9%
hyundai 133629
 
2.9%
bmw 119314
 
2.6%
Other values (6518) 1843353
40.1%

Most occurring characters

ValueCountFrequency (%)
A 3260460
 
12.3%
O 2946641
 
11.1%
D 2086862
 
7.9%
E 1993632
 
7.5%
K 1648512
 
6.2%
N 1311821
 
4.9%
R 1153407
 
4.3%
T 1126992
 
4.2%
Š 1108416
 
4.2%
S 1039882
 
3.9%
Other values (108) 8887502
33.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 26248331
98.8%
Dash Punctuation 154349
 
0.6%
Space Separator 141019
 
0.5%
Other Punctuation 7844
 
< 0.1%
Decimal Number 6942
 
< 0.1%
Lowercase Letter 5369
 
< 0.1%
Math Symbol 153
 
< 0.1%
Close Punctuation 57
 
< 0.1%
Open Punctuation 51
 
< 0.1%
Modifier Symbol 11
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3260460
 
12.4%
O 2946641
 
11.2%
D 2086862
 
8.0%
E 1993632
 
7.6%
K 1648512
 
6.3%
N 1311821
 
5.0%
R 1153407
 
4.4%
T 1126992
 
4.3%
Š 1108416
 
4.2%
S 1039882
 
4.0%
Other values (40) 8571706
32.7%
Lowercase Letter
ValueCountFrequency (%)
e 726
13.5%
l 465
 
8.7%
a 440
 
8.2%
r 438
 
8.2%
o 377
 
7.0%
n 348
 
6.5%
c 303
 
5.6%
s 255
 
4.7%
i 252
 
4.7%
t 210
 
3.9%
Other values (30) 1555
29.0%
Decimal Number
ValueCountFrequency (%)
0 1607
23.1%
5 1564
22.5%
1 1379
19.9%
2 755
10.9%
3 563
 
8.1%
6 396
 
5.7%
7 316
 
4.6%
8 184
 
2.7%
4 117
 
1.7%
9 61
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 6758
86.2%
/ 592
 
7.5%
& 255
 
3.3%
, 218
 
2.8%
* 13
 
0.2%
" 3
 
< 0.1%
§ 3
 
< 0.1%
' 1
 
< 0.1%
; 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 154313
> 99.9%
36
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 9
81.8%
¨ 2
 
18.2%
Space Separator
ValueCountFrequency (%)
141019
100.0%
Math Symbol
ValueCountFrequency (%)
+ 153
100.0%
Close Punctuation
ValueCountFrequency (%)
) 57
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26253700
98.8%
Common 310427
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3260460
 
12.4%
O 2946641
 
11.2%
D 2086862
 
7.9%
E 1993632
 
7.6%
K 1648512
 
6.3%
N 1311821
 
5.0%
R 1153407
 
4.4%
T 1126992
 
4.3%
Š 1108416
 
4.2%
S 1039882
 
4.0%
Other values (80) 8577075
32.7%
Common
ValueCountFrequency (%)
- 154313
49.7%
141019
45.4%
. 6758
 
2.2%
0 1607
 
0.5%
5 1564
 
0.5%
1 1379
 
0.4%
2 755
 
0.2%
/ 592
 
0.2%
3 563
 
0.2%
6 396
 
0.1%
Other values (18) 1481
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25212287
94.9%
None 1351803
 
5.1%
Punctuation 37
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3260460
12.9%
O 2946641
 
11.7%
D 2086862
 
8.3%
E 1993632
 
7.9%
K 1648512
 
6.5%
N 1311821
 
5.2%
R 1153407
 
4.6%
T 1126992
 
4.5%
S 1039882
 
4.1%
I 1029462
 
4.1%
Other values (64) 7614616
30.2%
None
ValueCountFrequency (%)
Š 1108416
82.0%
Ë 142198
 
10.5%
Í 31621
 
2.3%
Ý 31048
 
2.3%
Ü 13746
 
1.0%
Á 6859
 
0.5%
Ö 6359
 
0.5%
Č 5674
 
0.4%
Ě 1494
 
0.1%
Ř 1311
 
0.1%
Other values (32) 3077
 
0.2%
Punctuation
ValueCountFrequency (%)
36
97.3%
1
 
2.7%

DrVoz
Categorical

Distinct43
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size68.2 MiB
OSOBNÍ AUTOMOBIL
3315106 
NÁKLADNÍ AUTOMOBIL
520570 
MOTOCYKL
 
192509
NÁKLADNÍ PŘÍVĚS
 
174074
PŘÍPOJNÉ VOZIDLO
 
68752
Other values (38)
 
201264

Length

Max length30
Median length16
Mean length15.831025
Min length7

Characters and Unicode

Total characters70800697
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPŘÍVĚS TRAKTOROVÝ
2nd rowPŘÍVĚS TRAKTOROVÝ
3rd rowPŘÍVĚS TRAKTOROVÝ
4th rowPŘÍVĚS TRAKTOROVÝ
5th rowTRAKTOR KOLOVÝ

Common Values

ValueCountFrequency (%)
OSOBNÍ AUTOMOBIL 3315106
74.1%
NÁKLADNÍ AUTOMOBIL 520570
 
11.6%
MOTOCYKL 192509
 
4.3%
NÁKLADNÍ PŘÍVĚS 174074
 
3.9%
PŘÍPOJNÉ VOZIDLO 68752
 
1.5%
NÁKLADNÍ NÁVĚS 43719
 
1.0%
VOZIDLO ZVLÁŠTNÍHO URČENÍ 28966
 
0.6%
TRAKTOR KOLOVÝ 26848
 
0.6%
AUTOBUS 21406
 
0.5%
SPECIÁLNÍ AUTOMOBIL 16728
 
0.4%
Other values (33) 63597
 
1.4%

Length

2023-04-01T17:54:14.431448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
automobil 3852404
44.0%
osobní 3315106
37.8%
nákladní 744441
 
8.5%
přívěs 196948
 
2.2%
motocykl 192509
 
2.2%
vozidlo 103542
 
1.2%
přípojné 68752
 
0.8%
návěs 50181
 
0.6%
traktor 38447
 
0.4%
zvláštního 28966
 
0.3%
Other values (35) 169427
 
1.9%

Most occurring characters

ValueCountFrequency (%)
O 15195962
21.5%
B 7189541
10.2%
N 5031467
 
7.1%
L 4975725
 
7.0%
A 4711089
 
6.7%
Í 4421526
 
6.2%
4288540
 
6.1%
T 4225281
 
6.0%
M 4050352
 
5.7%
I 3982203
 
5.6%
Other values (23) 12729011
18.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66508937
93.9%
Space Separator 4288540
 
6.1%
Other Punctuation 3220
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 15195962
22.8%
B 7189541
10.8%
N 5031467
 
7.6%
L 4975725
 
7.5%
A 4711089
 
7.1%
Í 4421526
 
6.6%
T 4225281
 
6.4%
M 4050352
 
6.1%
I 3982203
 
6.0%
U 3928374
 
5.9%
Other values (21) 8797417
13.2%
Space Separator
ValueCountFrequency (%)
4288540
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3220
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66508937
93.9%
Common 4291760
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 15195962
22.8%
B 7189541
10.8%
N 5031467
 
7.6%
L 4975725
 
7.5%
A 4711089
 
7.1%
Í 4421526
 
6.6%
T 4225281
 
6.4%
M 4050352
 
6.1%
I 3982203
 
6.0%
U 3928374
 
5.9%
Other values (21) 8797417
13.2%
Common
ValueCountFrequency (%)
4288540
99.9%
. 3220
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64804100
91.5%
None 5996597
 
8.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 15195962
23.4%
B 7189541
11.1%
N 5031467
 
7.8%
L 4975725
 
7.7%
A 4711089
 
7.3%
4288540
 
6.6%
T 4225281
 
6.5%
M 4050352
 
6.3%
I 3982203
 
6.1%
U 3928374
 
6.1%
Other values (13) 7225566
11.1%
None
ValueCountFrequency (%)
Í 4421526
73.7%
Á 857388
 
14.3%
Ř 266271
 
4.4%
Ě 254749
 
4.2%
É 73702
 
1.2%
Ý 50208
 
0.8%
Č 36443
 
0.6%
Š 28966
 
0.5%
Ů 7181
 
0.1%
Ž 163
 
< 0.1%

ObchOznTyp
Categorical

Distinct93656
Distinct (%)2.1%
Missing140
Missing (%)< 0.1%
Memory size68.2 MiB
OCTAVIA
 
138267
FABIA
 
124654
FABIA (5J)
 
100526
OCTAVIA (1Z)
 
94399
FABIA (6Y)
 
74094
Other values (93651)
3940196 

Length

Max length40
Median length34
Mean length8.4760251
Min length1

Characters and Unicode

Total characters37905937
Distinct characters123
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38664 ?
Unique (%)0.9%

Sample

1st rowP73S
2nd rowMV 2-028
3rd rowMV 2-028
4th rowP 93 S
5th row8011

Common Values

ValueCountFrequency (%)
OCTAVIA 138267
 
3.1%
FABIA 124654
 
2.8%
FABIA (5J) 100526
 
2.2%
OCTAVIA (1Z) 94399
 
2.1%
FABIA (6Y) 74094
 
1.7%
OCTAVIA (5E) 65555
 
1.5%
OCTAVIA (1U) 58116
 
1.3%
GOLF 47689
 
1.1%
SUPERB (3T) 44978
 
1.0%
FELICIA 44721
 
1.0%
Other values (93646) 3679137
82.3%

Length

2023-04-01T17:54:14.523897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
octavia 431430
 
5.7%
fabia 360496
 
4.8%
5j 127306
 
1.7%
1z 120091
 
1.6%
6y 112327
 
1.5%
golf 110764
 
1.5%
combi 108969
 
1.4%
passat 95539
 
1.3%
95111
 
1.3%
5e 85103
 
1.1%
Other values (45886) 5876535
78.1%

Most occurring characters

ValueCountFrequency (%)
A 3969481
 
10.5%
3259375
 
8.6%
I 2005094
 
5.3%
O 1922834
 
5.1%
T 1795490
 
4.7%
( 1692528
 
4.5%
) 1690338
 
4.5%
C 1622225
 
4.3%
R 1572595
 
4.1%
S 1456562
 
3.8%
Other values (113) 16919415
44.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25712543
67.8%
Decimal Number 5024236
 
13.3%
Space Separator 3259375
 
8.6%
Open Punctuation 1692547
 
4.5%
Close Punctuation 1690357
 
4.5%
Dash Punctuation 201724
 
0.5%
Other Punctuation 153886
 
0.4%
Lowercase Letter 142730
 
0.4%
Modifier Symbol 25944
 
0.1%
Math Symbol 2533
 
< 0.1%
Other values (2) 62
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3969481
15.4%
I 2005094
 
7.8%
O 1922834
 
7.5%
T 1795490
 
7.0%
C 1622225
 
6.3%
R 1572595
 
6.1%
S 1456562
 
5.7%
E 1417400
 
5.5%
N 1071373
 
4.2%
F 920235
 
3.6%
Other values (37) 7959254
31.0%
Lowercase Letter
ValueCountFrequency (%)
i 68422
47.9%
r 8130
 
5.7%
e 7605
 
5.3%
x 7192
 
5.0%
a 7103
 
5.0%
d 5457
 
3.8%
n 4850
 
3.4%
o 4643
 
3.3%
v 4171
 
2.9%
t 2784
 
2.0%
Other values (30) 22373
 
15.7%
Other Punctuation
ValueCountFrequency (%)
. 78114
50.8%
/ 63008
40.9%
, 6004
 
3.9%
* 4870
 
3.2%
! 1666
 
1.1%
' 84
 
0.1%
; 67
 
< 0.1%
& 45
 
< 0.1%
" 17
 
< 0.1%
@ 7
 
< 0.1%
Other values (2) 4
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 1001402
19.9%
1 788760
15.7%
5 667155
13.3%
3 624505
12.4%
2 586293
11.7%
6 423329
8.4%
4 339610
 
6.8%
7 248902
 
5.0%
8 225338
 
4.5%
9 118942
 
2.4%
Math Symbol
ValueCountFrequency (%)
+ 2483
98.0%
| 48
 
1.9%
= 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1692528
> 99.9%
[ 19
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1690338
> 99.9%
] 19
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 201722
> 99.9%
2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 25919
99.9%
¨ 25
 
0.1%
Space Separator
ValueCountFrequency (%)
3259375
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 52
100.0%
Modifier Letter
ValueCountFrequency (%)
ˇ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 25855273
68.2%
Common 12050664
31.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3969481
15.4%
I 2005094
 
7.8%
O 1922834
 
7.4%
T 1795490
 
6.9%
C 1622225
 
6.3%
R 1572595
 
6.1%
S 1456562
 
5.6%
E 1417400
 
5.5%
N 1071373
 
4.1%
F 920235
 
3.6%
Other values (77) 8101984
31.3%
Common
ValueCountFrequency (%)
3259375
27.0%
( 1692528
14.0%
) 1690338
14.0%
0 1001402
 
8.3%
1 788760
 
6.5%
5 667155
 
5.5%
3 624505
 
5.2%
2 586293
 
4.9%
6 423329
 
3.5%
4 339610
 
2.8%
Other values (26) 977369
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37817503
99.8%
None 88422
 
0.2%
Modifier Letters 10
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3969481
 
10.5%
3259375
 
8.6%
I 2005094
 
5.3%
O 1922834
 
5.1%
T 1795490
 
4.7%
( 1692528
 
4.5%
) 1690338
 
4.5%
C 1622225
 
4.3%
R 1572595
 
4.2%
S 1456562
 
3.9%
Other values (74) 16830981
44.5%
None
ValueCountFrequency (%)
´ 25919
29.3%
Ý 23026
26.0%
Í 21599
24.4%
Á 10528
11.9%
á 2387
 
2.7%
É 2262
 
2.6%
Č 739
 
0.8%
Ü 465
 
0.5%
í 390
 
0.4%
Š 348
 
0.4%
Other values (27) 759
 
0.9%
Modifier Letters
ValueCountFrequency (%)
ˇ 10
100.0%
Punctuation
ValueCountFrequency (%)
2
100.0%

Ct
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct146
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size68.2 MiB
M1
3224921 
N1
 
319787
O1
 
157293
N3
 
120741
M1G
 
103650
Other values (141)
545883 

Length

Max length7
Median length2
Mean length2.0835353
Min length1

Characters and Unicode

Total characters9318143
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowRa3
2nd rowRa3
3rd rowRa3
4th rowRa3
5th rowT1a

Common Values

ValueCountFrequency (%)
M1 3224921
72.1%
N1 319787
 
7.2%
O1 157293
 
3.5%
N3 120741
 
2.7%
M1G 103650
 
2.3%
O4 87589
 
2.0%
L3e 75579
 
1.7%
LC 66985
 
1.5%
N2 59091
 
1.3%
O2 58408
 
1.3%
Other values (136) 198231
 
4.4%

Length

2023-04-01T17:54:14.611740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1 3224921
72.1%
n1 319787
 
7.2%
o1 157293
 
3.5%
n3 120741
 
2.7%
m1g 103650
 
2.3%
o4 87589
 
2.0%
l3e 75579
 
1.7%
lc 66985
 
1.5%
n2 59091
 
1.3%
o2 58408
 
1.3%
Other values (135) 198231
 
4.4%

Most occurring characters

ValueCountFrequency (%)
1 3887885
41.7%
M 3351193
36.0%
N 556026
 
6.0%
O 320796
 
3.4%
3 281941
 
3.0%
L 192931
 
2.1%
G 157990
 
1.7%
2 130322
 
1.4%
e 109892
 
1.2%
4 100181
 
1.1%
Other values (23) 228986
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4760644
51.1%
Decimal Number 4406863
47.3%
Lowercase Letter 125755
 
1.3%
Dash Punctuation 24339
 
0.3%
Space Separator 386
 
< 0.1%
Other Punctuation 156
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 3351193
70.4%
N 556026
 
11.7%
O 320796
 
6.7%
L 192931
 
4.1%
G 157990
 
3.3%
C 67104
 
1.4%
T 51397
 
1.1%
A 32691
 
0.7%
S 10913
 
0.2%
R 6823
 
0.1%
Other values (7) 12780
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 3887885
88.2%
3 281941
 
6.4%
2 130322
 
3.0%
4 100181
 
2.3%
7 4475
 
0.1%
5 1401
 
< 0.1%
6 658
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 109892
87.4%
a 13091
 
10.4%
b 2735
 
2.2%
z 36
 
< 0.1%
p 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 146
93.6%
* 10
 
6.4%
Dash Punctuation
ValueCountFrequency (%)
- 24339
100.0%
Space Separator
ValueCountFrequency (%)
386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4886399
52.4%
Common 4431744
47.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 3351193
68.6%
N 556026
 
11.4%
O 320796
 
6.6%
L 192931
 
3.9%
G 157990
 
3.2%
e 109892
 
2.2%
C 67104
 
1.4%
T 51397
 
1.1%
A 32691
 
0.7%
a 13091
 
0.3%
Other values (12) 33288
 
0.7%
Common
ValueCountFrequency (%)
1 3887885
87.7%
3 281941
 
6.4%
2 130322
 
2.9%
4 100181
 
2.3%
- 24339
 
0.5%
7 4475
 
0.1%
5 1401
 
< 0.1%
6 658
 
< 0.1%
386
 
< 0.1%
. 146
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9318143
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3887885
41.7%
M 3351193
36.0%
N 556026
 
6.0%
O 320796
 
3.4%
3 281941
 
3.0%
L 192931
 
2.1%
G 157990
 
1.7%
2 130322
 
1.4%
e 109892
 
1.2%
4 100181
 
1.1%
Other values (23) 228986
 
2.5%
Distinct22599
Distinct (%)0.5%
Missing26542
Missing (%)0.6%
Memory size68.2 MiB
Minimum1753-06-13 00:00:00
Maximum2030-09-04 00:00:00
2023-04-01T17:54:14.695561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:54:14.785270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Km
Real number (ℝ)

Distinct531333
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167128.29
Minimum0
Maximum9901410
Zeros349677
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:14.875882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q164147
median150729
Q3235589
95-th percentile385459
Maximum9901410
Range9901410
Interquartile range (IQR)171442

Descriptive statistics

Standard deviation147230.88
Coefficient of variation (CV)0.88094528
Kurtosis111.10898
Mean167128.29
Median Absolute Deviation (MAD)85760
Skewness4.3573436
Sum7.4744382 × 1011
Variance2.1676931 × 1010
MonotonicityNot monotonic
2023-04-01T17:54:14.972850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 349677
 
7.8%
1 1840
 
< 0.1%
4 564
 
< 0.1%
10 554
 
< 0.1%
5 553
 
< 0.1%
9 516
 
< 0.1%
7 516
 
< 0.1%
8 509
 
< 0.1%
2 453
 
< 0.1%
6 452
 
< 0.1%
Other values (531323) 4116642
92.0%
ValueCountFrequency (%)
0 349677
7.8%
1 1840
 
< 0.1%
2 453
 
< 0.1%
3 437
 
< 0.1%
4 564
 
< 0.1%
5 553
 
< 0.1%
6 452
 
< 0.1%
7 516
 
< 0.1%
8 509
 
< 0.1%
9 516
 
< 0.1%
ValueCountFrequency (%)
9901410 1
< 0.1%
9819123 1
< 0.1%
9684985 1
< 0.1%
9650650 2
< 0.1%
9571978 1
< 0.1%
9557372 1
< 0.1%
9482067 1
< 0.1%
9191764 1
< 0.1%
8996264 1
< 0.1%
8847951 1
< 0.1%

VyslSTK
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size68.2 MiB
způsobilé
4192011 
částečně způsobilé
 
252658
nezpůsobilé
 
27606

Length

Max length18
Median length9
Mean length9.520794
Min length9

Characters and Unicode

Total characters42579609
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowzpůsobilé
2nd rowzpůsobilé
3rd rowzpůsobilé
4th rowzpůsobilé
5th rowzpůsobilé

Common Values

ValueCountFrequency (%)
způsobilé 4192011
93.7%
částečně způsobilé 252658
 
5.6%
nezpůsobilé 27606
 
0.6%
(Missing) 1
 
< 0.1%

Length

2023-04-01T17:54:15.052116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T17:54:15.130021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
způsobilé 4444669
94.1%
částečně 252658
 
5.3%
nezpůsobilé 27606
 
0.6%

Most occurring characters

ValueCountFrequency (%)
s 4724933
11.1%
z 4472275
10.5%
p 4472275
10.5%
ů 4472275
10.5%
o 4472275
10.5%
b 4472275
10.5%
i 4472275
10.5%
l 4472275
10.5%
é 4472275
10.5%
č 505316
 
1.2%
Other values (6) 1571160
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42326951
99.4%
Space Separator 252658
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 4724933
11.2%
z 4472275
10.6%
p 4472275
10.6%
ů 4472275
10.6%
o 4472275
10.6%
b 4472275
10.6%
i 4472275
10.6%
l 4472275
10.6%
é 4472275
10.6%
č 505316
 
1.2%
Other values (5) 1318502
 
3.1%
Space Separator
ValueCountFrequency (%)
252658
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 42326951
99.4%
Common 252658
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 4724933
11.2%
z 4472275
10.6%
p 4472275
10.6%
ů 4472275
10.6%
o 4472275
10.6%
b 4472275
10.6%
i 4472275
10.6%
l 4472275
10.6%
é 4472275
10.6%
č 505316
 
1.2%
Other values (5) 1318502
 
3.1%
Common
ValueCountFrequency (%)
252658
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32624427
76.6%
None 9955182
 
23.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 4724933
14.5%
z 4472275
13.7%
p 4472275
13.7%
o 4472275
13.7%
b 4472275
13.7%
i 4472275
13.7%
l 4472275
13.7%
e 280264
 
0.9%
n 280264
 
0.9%
t 252658
 
0.8%
None
ValueCountFrequency (%)
ů 4472275
44.9%
é 4472275
44.9%
č 505316
 
5.1%
á 252658
 
2.5%
ě 252658
 
2.5%

VyslEmise
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4472276
Missing (%)100.0%
Memory size68.2 MiB

DTKont
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8652659
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:15.189189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3898613
Coefficient of variation (CV)0.48507234
Kurtosis-1.0920782
Mean2.8652659
Median Absolute Deviation (MAD)1
Skewness0.17330495
Sum12814260
Variance1.9317143
MonotonicityNot monotonic
2023-04-01T17:54:15.244099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 986009
22.0%
1 973273
21.8%
2 954280
21.3%
4 865451
19.4%
5 648514
14.5%
6 43217
 
1.0%
7 1532
 
< 0.1%
ValueCountFrequency (%)
1 973273
21.8%
2 954280
21.3%
3 986009
22.0%
4 865451
19.4%
5 648514
14.5%
6 43217
 
1.0%
7 1532
 
< 0.1%
ValueCountFrequency (%)
7 1532
 
< 0.1%
6 43217
 
1.0%
5 648514
14.5%
4 865451
19.4%
3 986009
22.0%
2 954280
21.3%
1 973273
21.8%

ZavA
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8283456
Minimum0
Maximum30
Zeros2174461
Zeros (%)48.6%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:15.317240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4322305
Coefficient of variation (CV)1.3302904
Kurtosis2.313272
Mean1.8283456
Median Absolute Deviation (MAD)1
Skewness1.4912228
Sum8176866
Variance5.9157452
MonotonicityNot monotonic
2023-04-01T17:54:15.390693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 2174461
48.6%
1 484468
 
10.8%
2 427714
 
9.6%
3 380066
 
8.5%
4 322178
 
7.2%
5 272456
 
6.1%
6 161970
 
3.6%
7 101810
 
2.3%
8 63110
 
1.4%
9 35333
 
0.8%
Other values (21) 48710
 
1.1%
ValueCountFrequency (%)
0 2174461
48.6%
1 484468
 
10.8%
2 427714
 
9.6%
3 380066
 
8.5%
4 322178
 
7.2%
5 272456
 
6.1%
6 161970
 
3.6%
7 101810
 
2.3%
8 63110
 
1.4%
9 35333
 
0.8%
ValueCountFrequency (%)
30 2
 
< 0.1%
29 2
 
< 0.1%
28 9
 
< 0.1%
27 8
 
< 0.1%
26 5
 
< 0.1%
25 5
 
< 0.1%
24 6
 
< 0.1%
23 12
 
< 0.1%
22 37
< 0.1%
21 58
< 0.1%

ZavB
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15534149
Minimum0
Maximum29
Zeros4198756
Zeros (%)93.9%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:15.471750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78608022
Coefficient of variation (CV)5.0603367
Kurtosis81.182259
Mean0.15534149
Median Absolute Deviation (MAD)0
Skewness7.6358674
Sum694730
Variance0.61792211
MonotonicityNot monotonic
2023-04-01T17:54:15.543546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 4198756
93.9%
1 109644
 
2.5%
2 62628
 
1.4%
3 40148
 
0.9%
4 24359
 
0.5%
5 14584
 
0.3%
6 8816
 
0.2%
7 5047
 
0.1%
8 3085
 
0.1%
9 1874
 
< 0.1%
Other values (19) 3335
 
0.1%
ValueCountFrequency (%)
0 4198756
93.9%
1 109644
 
2.5%
2 62628
 
1.4%
3 40148
 
0.9%
4 24359
 
0.5%
5 14584
 
0.3%
6 8816
 
0.2%
7 5047
 
0.1%
8 3085
 
0.1%
9 1874
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
27 2
 
< 0.1%
26 3
 
< 0.1%
25 2
 
< 0.1%
24 4
 
< 0.1%
23 5
 
< 0.1%
22 11
 
< 0.1%
21 11
 
< 0.1%
20 20
< 0.1%
19 28
< 0.1%

ZavC
Real number (ℝ)

SKEWED  ZEROS 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0077179941
Minimum0
Maximum14
Zeros4447578
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:15.612185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11934777
Coefficient of variation (CV)15.463574
Kurtosis820.64706
Mean0.0077179941
Median Absolute Deviation (MAD)0
Skewness22.973759
Sum34517
Variance0.014243891
MonotonicityNot monotonic
2023-04-01T17:54:15.671821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 4447578
99.4%
1 17799
 
0.4%
2 5013
 
0.1%
3 1309
 
< 0.1%
4 350
 
< 0.1%
5 123
 
< 0.1%
6 48
 
< 0.1%
7 23
 
< 0.1%
8 14
 
< 0.1%
9 9
 
< 0.1%
Other values (4) 10
 
< 0.1%
ValueCountFrequency (%)
0 4447578
99.4%
1 17799
 
0.4%
2 5013
 
0.1%
3 1309
 
< 0.1%
4 350
 
< 0.1%
5 123
 
< 0.1%
6 48
 
< 0.1%
7 23
 
< 0.1%
8 14
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 1
 
< 0.1%
11 2
 
< 0.1%
10 6
 
< 0.1%
9 9
 
< 0.1%
8 14
 
< 0.1%
7 23
 
< 0.1%
6 48
 
< 0.1%
5 123
 
< 0.1%
4 350
< 0.1%

Zav0
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.068805011
Minimum0
Maximum6
Zeros4183776
Zeros (%)93.5%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:15.737247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.27007942
Coefficient of variation (CV)3.925287
Kurtosis18.192425
Mean0.068805011
Median Absolute Deviation (MAD)0
Skewness4.1110896
Sum307715
Variance0.072942891
MonotonicityNot monotonic
2023-04-01T17:54:15.793648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4183776
93.5%
1 269836
 
6.0%
2 18175
 
0.4%
3 437
 
< 0.1%
4 43
 
< 0.1%
5 8
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 4183776
93.5%
1 269836
 
6.0%
2 18175
 
0.4%
3 437
 
< 0.1%
4 43
 
< 0.1%
5 8
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 8
 
< 0.1%
4 43
 
< 0.1%
3 437
 
< 0.1%
2 18175
 
0.4%
1 269836
 
6.0%
0 4183776
93.5%

Zav1
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42908085
Minimum0
Maximum17
Zeros3217168
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:15.862039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80838294
Coefficient of variation (CV)1.8839874
Kurtosis6.5111405
Mean0.42908085
Median Absolute Deviation (MAD)0
Skewness2.270826
Sum1918968
Variance0.65348298
MonotonicityNot monotonic
2023-04-01T17:54:15.926789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3217168
71.9%
1 774468
 
17.3%
2 344530
 
7.7%
3 101700
 
2.3%
4 25744
 
0.6%
5 6090
 
0.1%
6 1693
 
< 0.1%
7 562
 
< 0.1%
8 198
 
< 0.1%
9 64
 
< 0.1%
Other values (7) 59
 
< 0.1%
ValueCountFrequency (%)
0 3217168
71.9%
1 774468
 
17.3%
2 344530
 
7.7%
3 101700
 
2.3%
4 25744
 
0.6%
5 6090
 
0.1%
6 1693
 
< 0.1%
7 562
 
< 0.1%
8 198
 
< 0.1%
9 64
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
16 1
 
< 0.1%
14 5
 
< 0.1%
13 1
 
< 0.1%
12 7
 
< 0.1%
11 22
 
< 0.1%
10 22
 
< 0.1%
9 64
 
< 0.1%
8 198
 
< 0.1%
7 562
< 0.1%

Zav2
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087075798
Minimum0
Maximum7
Zeros4116379
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:15.995570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3094664
Coefficient of variation (CV)3.5539887
Kurtosis17.166312
Mean0.087075798
Median Absolute Deviation (MAD)0
Skewness3.8651192
Sum389427
Variance0.095769454
MonotonicityNot monotonic
2023-04-01T17:54:16.051147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4116379
92.0%
1 324930
 
7.3%
2 28659
 
0.6%
3 2090
 
< 0.1%
4 190
 
< 0.1%
5 20
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 4116379
92.0%
1 324930
 
7.3%
2 28659
 
0.6%
3 2090
 
< 0.1%
4 190
 
< 0.1%
5 20
 
< 0.1%
6 7
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 7
 
< 0.1%
5 20
 
< 0.1%
4 190
 
< 0.1%
3 2090
 
< 0.1%
2 28659
 
0.6%
1 324930
 
7.3%
0 4116379
92.0%

Zav3
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.094059043
Minimum0
Maximum7
Zeros4106450
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:16.116015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33732745
Coefficient of variation (CV)3.5863372
Kurtosis21.773099
Mean0.094059043
Median Absolute Deviation (MAD)0
Skewness4.1925961
Sum420658
Variance0.11378981
MonotonicityNot monotonic
2023-04-01T17:54:16.172920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4106450
91.8%
1 318843
 
7.1%
2 40185
 
0.9%
3 5894
 
0.1%
4 780
 
< 0.1%
5 103
 
< 0.1%
6 19
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 4106450
91.8%
1 318843
 
7.1%
2 40185
 
0.9%
3 5894
 
0.1%
4 780
 
< 0.1%
5 103
 
< 0.1%
6 19
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 19
 
< 0.1%
5 103
 
< 0.1%
4 780
 
< 0.1%
3 5894
 
0.1%
2 40185
 
0.9%
1 318843
 
7.1%
0 4106450
91.8%

Zav4
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31401617
Minimum0
Maximum14
Zeros3458680
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:16.238761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.67409385
Coefficient of variation (CV)2.1466851
Kurtosis11.696121
Mean0.31401617
Median Absolute Deviation (MAD)0
Skewness2.8214787
Sum1404367
Variance0.45440252
MonotonicityNot monotonic
2023-04-01T17:54:16.300323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 3458680
77.3%
1 720921
 
16.1%
2 223109
 
5.0%
3 51243
 
1.1%
4 12114
 
0.3%
5 3836
 
0.1%
6 1435
 
< 0.1%
7 552
 
< 0.1%
8 217
 
< 0.1%
9 88
 
< 0.1%
Other values (5) 81
 
< 0.1%
ValueCountFrequency (%)
0 3458680
77.3%
1 720921
 
16.1%
2 223109
 
5.0%
3 51243
 
1.1%
4 12114
 
0.3%
5 3836
 
0.1%
6 1435
 
< 0.1%
7 552
 
< 0.1%
8 217
 
< 0.1%
9 88
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 2
 
< 0.1%
12 10
 
< 0.1%
11 21
 
< 0.1%
10 47
 
< 0.1%
9 88
 
< 0.1%
8 217
 
< 0.1%
7 552
 
< 0.1%
6 1435
 
< 0.1%
5 3836
0.1%

Zav5
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18228437
Minimum0
Maximum10
Zeros3791040
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:16.371820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.46849841
Coefficient of variation (CV)2.5701513
Kurtosis11.534765
Mean0.18228437
Median Absolute Deviation (MAD)0
Skewness2.998105
Sum815226
Variance0.21949076
MonotonicityNot monotonic
2023-04-01T17:54:16.437083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 3791040
84.8%
1 565596
 
12.6%
2 101164
 
2.3%
3 11564
 
0.3%
4 2182
 
< 0.1%
5 563
 
< 0.1%
6 120
 
< 0.1%
7 36
 
< 0.1%
8 6
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 3791040
84.8%
1 565596
 
12.6%
2 101164
 
2.3%
3 11564
 
0.3%
4 2182
 
< 0.1%
5 563
 
< 0.1%
6 120
 
< 0.1%
7 36
 
< 0.1%
8 6
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 3
 
< 0.1%
8 6
 
< 0.1%
7 36
 
< 0.1%
6 120
 
< 0.1%
5 563
 
< 0.1%
4 2182
 
< 0.1%
3 11564
 
0.3%
2 101164
 
2.3%
1 565596
12.6%

Zav6
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78583902
Minimum0
Maximum17
Zeros2525840
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:16.503426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1100583
Coefficient of variation (CV)1.4125772
Kurtosis3.2851774
Mean0.78583902
Median Absolute Deviation (MAD)0
Skewness1.6230901
Sum3514489
Variance1.2322293
MonotonicityNot monotonic
2023-04-01T17:54:16.567192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2525840
56.5%
1 925874
 
20.7%
2 647819
 
14.5%
3 254864
 
5.7%
4 80562
 
1.8%
5 25048
 
0.6%
6 7840
 
0.2%
7 2782
 
0.1%
8 993
 
< 0.1%
9 359
 
< 0.1%
Other values (8) 295
 
< 0.1%
ValueCountFrequency (%)
0 2525840
56.5%
1 925874
 
20.7%
2 647819
 
14.5%
3 254864
 
5.7%
4 80562
 
1.8%
5 25048
 
0.6%
6 7840
 
0.2%
7 2782
 
0.1%
8 993
 
< 0.1%
9 359
 
< 0.1%
ValueCountFrequency (%)
17 2
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 8
 
< 0.1%
13 19
 
< 0.1%
12 36
 
< 0.1%
11 61
 
< 0.1%
10 165
 
< 0.1%
9 359
 
< 0.1%
8 993
< 0.1%

Zav7
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0073193157
Minimum0
Maximum6
Zeros4442373
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:16.640678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.093371567
Coefficient of variation (CV)12.756871
Kurtosis284.0495
Mean0.0073193157
Median Absolute Deviation (MAD)0
Skewness14.949235
Sum32734
Variance0.0087182496
MonotonicityNot monotonic
2023-04-01T17:54:16.696589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4442373
99.3%
1 27410
 
0.6%
2 2222
 
< 0.1%
3 214
 
< 0.1%
4 49
 
< 0.1%
5 6
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 4442373
99.3%
1 27410
 
0.6%
2 2222
 
< 0.1%
3 214
 
< 0.1%
4 49
 
< 0.1%
5 6
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 6
 
< 0.1%
4 49
 
< 0.1%
3 214
 
< 0.1%
2 2222
 
< 0.1%
1 27410
 
0.6%
0 4442373
99.3%

Zav8
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.022267856
Minimum0
Maximum8
Zeros4410567
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:16.761552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20950167
Coefficient of variation (CV)9.4082547
Kurtosis154.44313
Mean0.022267856
Median Absolute Deviation (MAD)0
Skewness11.426334
Sum99588
Variance0.043890948
MonotonicityNot monotonic
2023-04-01T17:54:16.822832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4410567
98.6%
1 33135
 
0.7%
2 21080
 
0.5%
3 6048
 
0.1%
4 1160
 
< 0.1%
5 227
 
< 0.1%
6 41
 
< 0.1%
7 16
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 4410567
98.6%
1 33135
 
0.7%
2 21080
 
0.5%
3 6048
 
0.1%
4 1160
 
< 0.1%
5 227
 
< 0.1%
6 41
 
< 0.1%
7 16
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 16
 
< 0.1%
6 41
 
< 0.1%
5 227
 
< 0.1%
4 1160
 
< 0.1%
3 6048
 
0.1%
2 21080
 
0.5%
1 33135
 
0.7%
0 4410567
98.6%

Zav9
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00069271217
Minimum0
Maximum5
Zeros4469856
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size68.2 MiB
2023-04-01T17:54:16.887973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.032780912
Coefficient of variation (CV)47.322559
Kurtosis4496.6886
Mean0.00069271217
Median Absolute Deviation (MAD)0
Skewness60.048234
Sum3098
Variance0.0010745882
MonotonicityNot monotonic
2023-04-01T17:54:16.948629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4469856
99.9%
1 1890
 
< 0.1%
2 410
 
< 0.1%
3 93
 
< 0.1%
4 26
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 4469856
99.9%
1 1890
 
< 0.1%
2 410
 
< 0.1%
3 93
 
< 0.1%
4 26
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 26
 
< 0.1%
3 93
 
< 0.1%
2 410
 
< 0.1%
1 1890
 
< 0.1%
0 4469856
99.9%

StariDnu
Real number (ℝ)

Distinct24492
Distinct (%)0.6%
Missing26542
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5362.7833
Minimum-2714
Maximum98462
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)< 0.1%
Memory size68.2 MiB
2023-04-01T17:54:17.027835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2714
5-th percentile1569
Q13047
median5244
Q36959
95-th percentile10209
Maximum98462
Range101176
Interquartile range (IQR)3912

Descriptive statistics

Standard deviation3197.3409
Coefficient of variation (CV)0.59620924
Kurtosis9.2442594
Mean5362.7833
Median Absolute Deviation (MAD)2024
Skewness1.8159967
Sum2.3841508 × 1010
Variance10222989
MonotonicityNot monotonic
2023-04-01T17:54:17.118301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8776 3616
 
0.1%
8411 3244
 
0.1%
1675 3214
 
0.1%
9141 3181
 
0.1%
9506 3140
 
0.1%
1682 2852
 
0.1%
1661 2741
 
0.1%
1689 2701
 
0.1%
1678 2600
 
0.1%
1654 2551
 
0.1%
Other values (24482) 4415894
98.7%
(Missing) 26542
 
0.6%
ValueCountFrequency (%)
-2714 1
 
< 0.1%
-2713 1
 
< 0.1%
-2646 2
 
< 0.1%
-2585 1
 
< 0.1%
-2547 1
 
< 0.1%
122 8
 
< 0.1%
123 8
 
< 0.1%
124 30
< 0.1%
127 14
< 0.1%
128 20
< 0.1%
ValueCountFrequency (%)
98462 1
 
< 0.1%
98393 1
 
< 0.1%
98320 1
 
< 0.1%
98294 1
 
< 0.1%
49446 1
 
< 0.1%
45015 425
< 0.1%
44935 176
< 0.1%
44926 1
 
< 0.1%
44920 1
 
< 0.1%
44885 1
 
< 0.1%

Interactions

2023-04-01T17:53:40.750214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:30.771521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:35.425227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:40.066665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:45.069229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:49.620905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:54.119417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:58.664734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:03.441324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:07.920447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:12.448760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:17.162504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:21.828972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:26.793231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:31.337189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:35.962660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:41.102631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:31.074699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:35.690375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:40.371708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:45.357830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:49.896224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:54.398241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:58.961437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:03.715556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:08.197444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:12.739722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:17.442132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:22.143950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:27.067507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:31.613867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:36.254774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:41.453875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:31.362108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:35.968770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:40.666238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:45.639739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:50.172550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:54.676836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:59.257517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:03.990969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:08.472924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:13.030421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:17.721510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:22.461548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:27.347862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:31.899597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:36.555471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:41.810756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:31.655811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:36.260482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:40.981790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:45.911118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:50.452892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:54.959461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:59.557193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:04.266290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:08.751190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:13.328386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:17.999121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:22.760931image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:27.628270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:32.180992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:36.836237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:42.165121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:31.940797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:36.555179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:41.297478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:46.190891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:50.717328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:55.236942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:59.856815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:04.543418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:09.029564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:13.618081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:18.277348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:23.067966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:27.906168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:32.462460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:37.125452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:42.522633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:32.230988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:36.855927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:41.601357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:46.476639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:51.003353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:55.509422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:00.155189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:04.821277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:09.312187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:13.915433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:18.567635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:23.392529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:28.196638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:32.747301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:37.418289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:42.875947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:32.537519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:37.133640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:41.893477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:46.756752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:51.280701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:55.787973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:00.441165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:05.099193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:09.587306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:14.205000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:18.863880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:23.710786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:28.485523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:33.024907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:37.704378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:43.232718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:32.829537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:37.431716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:42.202008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:47.042255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:51.565052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:56.076894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:00.738504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:05.370473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:09.869648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:14.503506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:19.149345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:24.013550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:28.773911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:33.314973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:37.989944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:43.589783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:33.113801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:37.728779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:42.505466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:47.327441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:51.847306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:56.360245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:01.040207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:05.650179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:10.137879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:14.797878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:19.431623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:24.318080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:29.062067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:33.605476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:38.272925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:43.946284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:33.400308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:38.025723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:42.800825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:47.610796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:52.127008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:56.644185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:01.342138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:05.928966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:10.419028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:15.083512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:19.726382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:24.631050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:29.342944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:33.893970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:38.574073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:44.300822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:33.690848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:38.305169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:43.128460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:47.894866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:52.406790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:56.930666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:01.640116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:06.207578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:10.704530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:15.376863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:20.030648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:24.940476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:29.628404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:34.184618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:38.886764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:44.676968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:33.970916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:38.583859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:43.453593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:48.170136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:52.680963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:57.205885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:01.929235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:06.480649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:10.987684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:15.666378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:20.305408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:25.236650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:29.904848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:34.479345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:39.189167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:45.046425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:34.253981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:38.862473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:43.782882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:48.452436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:52.959255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:57.487030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:02.226409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:06.761058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:11.269004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:15.962007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:20.591038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:25.553717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:30.177366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:34.791404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:39.473616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:45.402242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:34.533727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:39.154478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:44.094250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:48.731988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:53.236219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:57.764741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:02.522805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:07.038992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:11.550367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:16.251769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:20.885232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:25.851927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:30.453265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:35.067303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:39.778442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:45.781400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:34.812660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:39.435929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:44.409519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:49.016438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:53.515142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:58.047658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:02.821714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:07.318796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:11.832539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:16.548181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:21.188038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:26.158734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:30.734792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:35.356918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:40.051168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:46.104398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:35.144263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:39.760754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:44.771783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:49.343007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:53.836382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:52:58.370701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:03.158030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:07.638301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:12.154371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:16.880059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:21.517353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:26.505915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:31.056584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:35.681211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T17:53:40.388510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-01T17:53:48.645820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-01T17:53:54.517936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-01T17:54:06.898597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKVyslEmiseDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu
STK
6711Evidenční kontrola51642022-01-02NaNBSSPŘÍVĚS TRAKTOROVÝP73SRa31972-07-270způsobiléNaN7000000000000018430.0
6711pravidelná174732022-01-02NaNSTS HUMENNEPŘÍVĚS TRAKTOROVÝMV 2-028Ra31990-08-290způsobiléNaN7300000000300011823.0
6711pravidelná178262022-01-02NaNSTS HUMENNEPŘÍVĚS TRAKTOROVÝMV 2-028Ra31990-08-130způsobiléNaN7400010010200011839.0
6711pravidelná91125332022-01-02NaNBSSPŘÍVĚS TRAKTOROVÝP 93 SRa31977-03-220způsobiléNaN7000000000000016731.0
6711pravidelná333442022-01-02Z 8001ZETORTRAKTOR KOLOVÝ8011T1a1980-01-240způsobiléNaN7200000010100015693.0
6711pravidelnáK734103473B2022-01-027303ZETORTRAKTOR KOLOVÝ7341 SUPER TURBOT1a2001-12-200částečně způsobiléNaN743000004021007692.0
6711pravidelná119602022-01-02Z 8001URSUSTRAKTOR KOLOVÝC 385T1a1974-10-300způsobiléNaN7100100000000017605.0
6711pravidelná162452022-01-02Z 6701ZETORTRAKTOR KOLOVÝ5645T1a1971-09-300způsobiléNaN7000000000000018731.0
6711pravidelná122322022-01-02Z 7001ZETORTRAKTOR KOLOVÝ7011T1a1982-04-200částečně způsobiléNaN7440020030210014876.0
6711pravidelná257322022-01-02Z 4001ZETORTRAKTOR KOLOVÝ4011T1a1965-05-130způsobiléNaN7000000000000021062.0
DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKVyslEmiseDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu
STK
7718Technická silniční kontrolaZCFA75D04026016052022-11-29NaNIVECONÁKLADNÍ AUTOMOBILEUROCARGON2NaT334763způsobiléNaN20000000000000NaN
7718Technická silniční kontrolaWDB964207103515802022-11-29NaNMERCEDES-BENZNÁKLADNÍ AUTOMOBILAROCSN3NaT132964způsobiléNaN20000000000000NaN
6813pravidelnáJCB5TALHK711968302022-11-30JCB444TA1JCBTRAKTOR KOLOVÝ531-70 AGRIT12007-06-25263způsobiléNaN310000001000005759.0
7719Technická silniční kontrolaYV2RT40A2GB7812492022-11-29NaNVOLVOTAHAČ NÁVĚSŮFHN3NaT0způsobiléNaN20000000000000NaN
7719Technická silniční kontrolaVAVKSK339MD4681582022-11-29NaNSCHWARZMÜLLERNÁKLADNÍ NÁVĚSKIS 3/EO4NaT0způsobiléNaN20000000000000NaN
7719Technická silniční kontrolaWK0S00024001579442022-11-29NaNKogelNÁKLADNÍ NÁVĚSjináO4NaT0způsobiléNaN20000000000000NaN
7719Technická silniční kontrolaVAVJS1339DD3314102022-11-29NaNSCHWARZM¨LLERNÁKLADNÍ NÁVĚSjináO4NaT0způsobiléNaN20000000000000NaN
7719Technická silniční kontrolaYS2R4X200020997402022-11-29NaNSCANIANÁKLADNÍ AUTOMOBILR490N3NaT568173způsobiléNaN20000000000000NaN
7719Technická silniční kontrolaVAVJS1339CD3140562022-11-29NaNSCHWARZMÜLLERNÁKLADNÍ NÁVĚSSPA 3/EO4NaT0způsobiléNaN20000000000000NaN
7719Technická silniční kontrolaWJMM1VTH4043625972022-11-29NaNIVECONÁKLADNÍ AUTOMOBILSTRALIS ACTIVE SPACEN3NaT1292652způsobiléNaN20000000000000NaN

Duplicate rows

Most frequently occurring

DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu# duplicates
41845Evidenční kontrolaVF3BERFNC860459322022-11-08RFNPEUGEOTOSOBNÍ AUTOMOBILEXPERTM12003-08-29211945částečně způsobilé201010000000007155.06
75742Evidenční kontrolaZCFC150D5052911162022-11-11F1CGL411BIVECONÁKLADNÍ AUTOMOBIL50C/35N12019-06-26394140způsobilé500000000000001375.06
78532Na žádost zákazníkaSUDNS3000001332792022-11-07NaNWIELTONPŘÍPOJNÉ VOZIDLONS3KO42022-09-220způsobilé10000000000000191.06
86747Před registracíWAUZZZ4L3FD0034772022-11-28CRCAUDIOSOBNÍ AUTOMOBILQ7 (4L)M1G2014-08-12200322částečně způsobilé112000000020103154.06
104015opakovanáTNT260S24WK0317422022-11-21T3B-928-60TATRANÁKLADNÍ AUTOMOBIL815N3G1998-01-0131812způsobilé1100012101140009221.06
105331opakovanáVF1KM0J0H346160212022-11-25K4M T 7RENAULTOSOBNÍ AUTOMOBILMEGANEM12005-10-27163197způsobilé520001000100006365.06
138847pravidelnáL4HGTBBP7F60055092022-11-281J1PE40QMB-2BEELINEMOTOCYKLTAPOL1e-A2016-08-0914390způsobilé100000000000002426.06
238523pravidelnáUU2TA0BQ002QB81132022-11-07G11 631OLTCITOSOBNÍ AUTOMOBILCLUBM11988-03-2188948způsobilé1900130110300012794.06
240050pravidelnáVF17RBF0A558967702022-11-07K9K E6RENAULTOSOBNÍ AUTOMOBILCLIOM12016-07-2856066způsobilé100000000000002438.06
50ADRVAVAHB3277H2509202022-11-21*****SCHWARZMÜLLERNÁKLADNÍ PŘÍVĚS ADRTH3EO42007-07-240způsobilé100000000000005730.04